Spatial-temporal enhanced abnormal behavior detection based on multi-channel coupling

To address the limitations of existing abnormal behavior detection models—particularly their inadequate feature representation and insufficient modeling of dynamic temporal characteristics—this paper proposes a multi-channel coupled spatio-temporal enhanced anomaly detection method. Built upon the SlowFast network architecture, the proposed approach integrates a multi-channel coupled spatial enhancement module into the slow pathway to strengthen static feature modeling, and a multi-channel coupled temporal enhancement module into the fast pathway to improve the discriminability of dynamic temporal features. Extensive experiments on three benchmark datasets—Violent Flow, Hockey Fight, and Real-life Violence Situations—demonstrate that the proposed method achieves prediction accuracies of 95.3%, 97.3%, and 94%, respectively, outperforming current state-of-the-art approaches. The results validate the superior feature representation capability and generalization performance of the proposed method in abnormal behavior recognition tasks.